An Integrated Model of Associative and Reinforcement Learning

Vladislav Veksler, Air Force Research Laboratory

Christopher Myers, Air Force Research Laboratory

Kevin Gluck, Air Force Research Laboratory

Abstract

Any successful attempt at explaining and replicating the
complexity and generality of human and animal learning will require the
integration of a variety of learning mechanisms. Here we introduce a
computational model which integrates associative learning and reinforcement
learning. We contrast the integrated model with associative learning and
reinforcement learning models in two simulation studies. The first simulation
demonstrates performance advantages for the integrated model in an environment
with a dynamic and complex reward structure. The second simulation contrasts the
performances of the three models in a classic latent learning experiment
(Blodgett, 1929), demonstrating advantages for the integrated model in predicting
and explaining the behavioral data.